<p>Развој&nbsp; проширене&nbsp; и&nbsp; виртуелне&nbsp; реалности&nbsp; у&nbsp; последњих неколико година створио је могућности за укључивање и примену ових технологија на свим нивоима образовања. Бројна истраживања истичу да&nbsp; имерзивне&nbsp; технологије&nbsp; могу&nbsp; позитивно&nbsp; утицати&nbsp; на исходе&nbsp; учења&nbsp; и&nbsp; мотивисаност&nbsp; ученика.&nbsp; Такође, географија&nbsp; се&nbsp; често&nbsp; издваја&nbsp; као&nbsp; предмет&nbsp; који посебно&nbsp; може&nbsp; да&nbsp; искористи&nbsp; те&nbsp; потенцијале&nbsp; за унапређење&nbsp; и&nbsp; осавремењавање&nbsp; наставне&nbsp; праксе. Међутим,&nbsp; поставља&nbsp; се&nbsp; питање&nbsp; спремности наставника&nbsp; и&nbsp; студената&nbsp; географије&nbsp; (наставног усмерења)&nbsp; да&nbsp; организују&nbsp; наставу&nbsp; са&nbsp; мобилним&nbsp; и имерзивним&nbsp; технологијама.&nbsp; Притом,&nbsp; неопходно&nbsp; је свеобухватно&nbsp; сагледати&nbsp; позитивне&nbsp; и&nbsp; негативне факторе који утичу или могу утицати на интеграцију ових технологија у географско образовање. Сходно<br />наведеном,&nbsp; ова&nbsp; дисертација&nbsp; разматра&nbsp; како,&nbsp; када&nbsp; и зашто користити проширену и виртуелну реалност у настави и учењу географије.</p> / <p>Razvoj&nbsp; proširene&nbsp; i&nbsp; virtuelne&nbsp; realnosti&nbsp; u&nbsp; poslednjih nekoliko godina stvorio je mogućnosti za uključivanje i primenu ovih tehnologija na svim nivoima obrazovanja. Brojna istraživanja ističu da&nbsp; imerzivne&nbsp; tehnologije&nbsp; mogu&nbsp; pozitivno&nbsp; uticati&nbsp; na ishode&nbsp; učenja&nbsp; i&nbsp; motivisanost&nbsp; učenika.&nbsp; Takođe, geografija&nbsp; se&nbsp; često&nbsp; izdvaja&nbsp; kao&nbsp; predmet&nbsp; koji posebno&nbsp; može&nbsp; da&nbsp; iskoristi&nbsp; te&nbsp; potencijale&nbsp; za unapređenje&nbsp; i&nbsp; osavremenjavanje&nbsp; nastavne&nbsp; prakse. Međutim,&nbsp; postavlja&nbsp; se&nbsp; pitanje&nbsp; spremnosti nastavnika&nbsp; i&nbsp; studenata&nbsp; geografije&nbsp; (nastavnog usmerenja)&nbsp; da&nbsp; organizuju&nbsp; nastavu&nbsp; sa&nbsp; mobilnim&nbsp; i imerzivnim&nbsp; tehnologijama.&nbsp; Pritom,&nbsp; neophodno&nbsp; je sveobuhvatno&nbsp; sagledati&nbsp; pozitivne&nbsp; i&nbsp; negativne faktore koji utiču ili mogu uticati na integraciju ovih tehnologija u geografsko obrazovanje. Shodno<br />navedenom,&nbsp; ova&nbsp; disertacija&nbsp; razmatra&nbsp; kako,&nbsp; kada&nbsp; i zašto koristiti proširenu i virtuelnu realnost u nastavi i učenju geografije.</p> / <p>In&nbsp; recent&nbsp; years,&nbsp; the&nbsp; development&nbsp; of&nbsp; augmented&nbsp; and&nbsp; virtual&nbsp; reality&nbsp; has&nbsp; created&nbsp; opportunities&nbsp; for&nbsp; integration and use of these technologies at all levels of&nbsp; education.Numerous&nbsp; studies&nbsp; showed&nbsp; that&nbsp; immersive&nbsp; technologies have the potential to improve student learning outcomes and&nbsp; motivation.&nbsp; Geography&nbsp; is&nbsp; also&nbsp; often&nbsp; indicated&nbsp; as&nbsp; a subject&nbsp; that&nbsp; can&nbsp; utilize&nbsp; these&nbsp; potentials&nbsp; to&nbsp; improve&nbsp; and modernize&nbsp; teaching&nbsp; practice.&nbsp; However,&nbsp; the&nbsp; integration raises the question of the readiness of geography pre- and in-service&nbsp; teachers&nbsp; to&nbsp; organize&nbsp; classes&nbsp; with&nbsp; mobile&nbsp; and immersive technologies. Also, a comprehensive overlook regarding the positive and negative factors that influence or&nbsp; may influence the integration of these technologies in geographic&nbsp; education&nbsp; is&nbsp; necessary.&nbsp; Accordingly,&nbsp; this dissertation&nbsp; examines&nbsp; how,&nbsp; when,&nbsp; and&nbsp; why&nbsp; to&nbsp; use augmented and virtual reality in geography teaching and&nbsp; learning.</p>

Undergraduate software engineering learners demonstrate a lack of motivation with face-to-face classroom education. Limited access to the Internet via PCs and laptops, hinders effective communication and collaboration. However, the majority of learners enrolled for studies in tertiary education, have cellphones and are proficient in the use of digital technology. A technology-enhanced m-learning solution is indicated. This research project evaluates the usability and user experience of an m-learning environment, custom-designed for a tertiary educational context and delivered by mobile handheld devices, features a synthesized framework of categories and criteria, and determines the nature and scope of an emergent digital divide. A design-based research model suited to the context of the study is implemented, gathering quantitative and qualitative data from experts and learners by survey questionnaires. Analysis of data highlights usability and UX problems, provides insight into an emergent digital divide and suggests guidelines specific to the design of m-learning implementations. / Educational Studies / M. Sc. (Information Systems)

Recent research has persistently emphasized that education is broken and that the solutions lie with technological resources in educational institutions and teaching methods (Teräs et al., 2020), which has generated a techno-solutionism approach of Edtech companies (Mirrlees &amp; Alvi, 2019). The outbreak of COVID-19 exposed significant challenges and limitations when adapting education to the digital environment highlighting a need for a deeper understanding of the integration of technology with education. While some scholars focus on technological artefacts as an object of study at the intersection between neuroscience and technology (Healy, 1998), others focus on the importance of pedagogy and the challenges of researching pedagogy in a context of rapidly increasing technological advancement (Hellstén &amp; Reid, 2008).Through investigating Brazilian teachers’ experiences and challenges of integrating technology within higher education before and during the pandemic, the study contributes to furthering understanding of the intersection between pedagogy and technology. The application of an exploratory sequential mixed-methods design contextualizes the teachers’ voices and enables contrast with the existing literature in international contexts. The study’s findings suggest that most of the teachers included technology in their classes to some extent. The teachers’ personal preferences and course discipline, however, can influence how they would incorporate technological resources in higher education. Moreover, the university’s policies and support structures also influence teachers’ decisions and motivations regarding technology integration. The study recontextualized the growing discussion in global research surrounding education and technology by using prominent scholars in the field to form a foundation through which to understand if similar issues may also appear in the Brazilian context. The main differences in the findings of this research set the case of Brazil apart in that the teachers noticed that the course discipline can be a factor in deciding whether or not to use technological resources in their classes at Brazilian institutions. The discussion about the course discipline did not appear in the international literature review focusing on technology-enhanced learning and teachers’ attitudes toward technology integrations. Moreover, this research facilitated the creation of the Inverted Mirror instrument, which was developed during the literature review process to create a visualization of comparisons. The Inverted Mirror instrument is used here to compare and visualize what is unseen or hidden during the comparison of teacher experiences in order to explain what occurs when technology is used in the classroom.

[ES] En los últimos años, el aprendizaje profundo ha cambiado significativamente el panorama en diversas áreas del campo de la inteligencia artificial, entre las que se incluyen la visión por computador, el procesamiento del lenguaje natural, robótica o teoría de juegos. En particular, el sorprendente éxito del aprendizaje profundo en múltiples aplicaciones del campo del procesamiento del lenguaje natural tales como el reconocimiento automático del habla (ASR), la traducción automática (MT) o la síntesis de voz (TTS), ha supuesto una mejora drástica en la precisión de estos sistemas, extendiendo así su implantación a un mayor rango de aplicaciones en la vida real. En este momento, es evidente que las tecnologías de reconocimiento automático del habla y traducción automática pueden ser empleadas para producir, de forma efectiva, subtítulos multilingües de alta calidad de contenidos audiovisuales. Esto es particularmente cierto en el contexto de los vídeos educativos, donde las condiciones acústicas son normalmente favorables para los sistemas de ASR y el discurso está gramaticalmente bien formado. Sin embargo, en el caso de TTS, aunque los sistemas basados en redes neuronales han demostrado ser capaces de sintetizar voz de un realismo y calidad sin precedentes, todavía debe comprobarse si esta tecnología está lo suficientemente madura como para mejorar la accesibilidad y la participación en el aprendizaje en línea. Además, existen diversas tareas en el campo de la síntesis de voz que todavía suponen un reto, como la clonación de voz inter-lingüe, la síntesis incremental o la adaptación zero-shot a nuevos locutores. Esta tesis aborda la mejora de las prestaciones de los sistemas actuales de síntesis de voz basados en redes neuronales, así como la extensión de su aplicación en diversos escenarios, en el contexto de mejorar la accesibilidad en el aprendizaje en línea. En este sentido, este trabajo presta especial atención a la adaptación a nuevos locutores y a la clonación de voz inter-lingüe, ya que los textos a sintetizar se corresponden, en este caso, a traducciones de intervenciones originalmente en otro idioma. / [CA] Durant aquests darrers anys, l'aprenentatge profund ha canviat significativament el panorama en diverses àrees del camp de la intel·ligència artificial, entre les quals s'inclouen la visió per computador, el processament del llenguatge natural, robòtica o la teoria de jocs. En particular, el sorprenent èxit de l'aprenentatge profund en múltiples aplicacions del camp del processament del llenguatge natural, com ara el reconeixement automàtic de la parla (ASR), la traducció automàtica (MT) o la síntesi de veu (TTS), ha suposat una millora dràstica en la precisió i qualitat d'aquests sistemes, estenent així la seva implantació a un ventall més ampli a la vida real. En aquest moment, és evident que les tecnologies de reconeixement automàtic de la parla i traducció automàtica poden ser emprades per a produir, de forma efectiva, subtítols multilingües d'alta qualitat de continguts audiovisuals. Això és particularment cert en el context dels vídeos educatius, on les condicions acústiques són normalment favorables per als sistemes d'ASR i el discurs està gramaticalment ben format. No obstant això, al cas de TTS, encara que els sistemes basats en xarxes neuronals han demostrat ser capaços de sintetitzar veu d'un realisme i qualitat sense precedents, encara s'ha de comprovar si aquesta tecnologia és ja prou madura com per millorar l'accessibilitat i la participació en l'aprenentatge en línia. A més, hi ha diverses tasques al camp de la síntesi de veu que encara suposen un repte, com ara la clonació de veu inter-lingüe, la síntesi incremental o l'adaptació zero-shot a nous locutors. Aquesta tesi aborda la millora de les prestacions dels sistemes actuals de síntesi de veu basats en xarxes neuronals, així com l'extensió de la seva aplicació en diversos escenaris, en el context de millorar l'accessibilitat en l'aprenentatge en línia. En aquest sentit, aquest treball presta especial atenció a l'adaptació a nous locutors i a la clonació de veu interlingüe, ja que els textos a sintetitzar es corresponen, en aquest cas, a traduccions d'intervencions originalment en un altre idioma. / [EN] In recent years, deep learning has fundamentally changed the landscapes of a number of areas in artificial intelligence, including computer vision, natural language processing, robotics, and game theory. In particular, the striking success of deep learning in a large variety of natural language processing (NLP) applications, including automatic speech recognition (ASR), machine translation (MT), and text-to-speech (TTS), has resulted in major accuracy improvements, thus widening the applicability of these technologies in real-life settings. At this point, it is clear that ASR and MT technologies can be utilized to produce cost-effective, high-quality multilingual subtitles of video contents of different kinds. This is particularly true in the case of transcription and translation of video lectures and other kinds of educational materials, in which the audio recording conditions are usually favorable for the ASR task, and there is a grammatically well-formed speech. However, although state-of-the-art neural approaches to TTS have shown to drastically improve the naturalness and quality of synthetic speech over conventional concatenative and parametric systems, it is still unclear whether this technology is already mature enough to improve accessibility and engagement in online learning, and particularly in the context of higher education. Furthermore, advanced topics in TTS such as cross-lingual voice cloning, incremental TTS or zero-shot speaker adaptation remain an open challenge in the field. This thesis is about enhancing the performance and widening the applicability of modern neural TTS technologies in real-life settings, both in offline and streaming conditions, in the context of improving accessibility and engagement in online learning. Thus, particular emphasis is placed on speaker adaptation and cross-lingual voice cloning, as the input text corresponds to a translated utterance in this context. / Pérez González De Martos, AM. (2022). Deep Neural Networks for Automatic Speech-To-Speech Translation of Open Educational Resources [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/184019 / Premios Extraordinarios de tesis doctorales

Tesis por compendio / [ES] Durante la última década, los medios de comunicación han experimentado una revolución, alejándose de la televisión convencional hacia las plataformas de contenido bajo demanda. Además, esta revolución no ha cambiado solamente la manera en la que nos entretenemos, si no también la manera en la que aprendemos. En este sentido, las plataformas de contenido educativo bajo demanda también han proliferado para proporcionar recursos educativos de diversos tipos. Estas nuevas vías de distribución de contenido han llegado con nuevos requisitos para mejorar la accesibilidad, en particular las relacionadas con las dificultades de audición y las barreras lingüísticas. Aquí radica la oportunidad para el reconocimiento automático del habla (RAH) para cumplir estos requisitos, proporcionando subtitulado automático de alta calidad. Este subtitulado proporciona una base sólida para reducir esta brecha de accesibilidad, especialmente para contenido en directo o streaming. Estos sistemas de streaming deben trabajar bajo estrictas condiciones de tiempo real, proporcionando la subtitulación tan rápido como sea posible, trabajando con un contexto limitado. Sin embargo, esta limitación puede conllevar una degradación de la calidad cuando se compara con los sistemas para contenido en diferido u offline. Esta tesis propone un sistema de RAH en streaming con baja latencia, con una calidad similar a un sistema offline. Concretamente, este trabajo describe el camino seguido desde el sistema offline híbrido inicial hasta el eficiente sistema final de reconocimiento en streaming. El primer paso es la adaptación del sistema para efectuar una sola iteración de reconocimiento haciendo uso de modelos de lenguaje estado del arte basados en redes neuronales. En los sistemas basados en múltiples iteraciones estos modelos son relegados a una segunda (o posterior) iteración por su gran coste computacional. Tras adaptar el modelo de lenguaje, el modelo acústico basado en redes neuronales también tiene que adaptarse para trabajar con un contexto limitado. La integración y la adaptación de estos modelos es ampliamente descrita en esta tesis, evaluando el sistema RAH resultante, completamente adaptado para streaming, en conjuntos de datos académicos extensamente utilizados y desafiantes tareas basadas en contenidos audiovisuales reales. Como resultado, el sistema proporciona bajas tasas de error con un reducido tiempo de respuesta, comparables al sistema offline. / [CA] Durant l'última dècada, els mitjans de comunicació han experimentat una revolució, allunyant-se de la televisió convencional cap a les plataformes de contingut sota demanda. A més a més, aquesta revolució no ha canviat només la manera en la que ens entretenim, si no també la manera en la que aprenem. En aquest sentit, les plataformes de contingut educatiu sota demanda també han proliferat pera proporcionar recursos educatius de diversos tipus. Aquestes noves vies de distribució de contingut han arribat amb nous requisits per a millorar l'accessibilitat, en particular les relacionades amb les dificultats d'audició i les barreres lingüístiques. Aquí radica l'oportunitat per al reconeixement automàtic de la parla (RAH) per a complir aquests requisits, proporcionant subtitulat automàtic d'alta qualitat. Aquest subtitulat proporciona una base sòlida per a reduir aquesta bretxa d'accessibilitat, especialment per a contingut en directe o streaming. Aquests sistemes han de treballar sota estrictes condicions de temps real, proporcionant la subtitulació tan ràpid com sigui possible, treballant en un context limitat. Aquesta limitació, però, pot comportar una degradació de la qualitat quan es compara amb els sistemes per a contingut en diferit o offline. Aquesta tesi proposa un sistema de RAH en streaming amb baixa latència, amb una qualitat similar a un sistema offline. Concretament, aquest treball descriu el camí seguit des del sistema offline híbrid inicial fins l'eficient sistema final de reconeixement en streaming. El primer pas és l'adaptació del sistema per a efectuar una sola iteració de reconeixement fent servir els models de llenguatge de l'estat de l'art basat en xarxes neuronals. En els sistemes basats en múltiples iteracions aquests models son relegades a una segona (o posterior) iteració pel seu gran cost computacional. Un cop el model de llenguatge s'ha adaptat, el model acústic basat en xarxes neuronals també s'ha d'adaptar per a treballar amb un context limitat. La integració i l'adaptació d'aquests models és àmpliament descrita en aquesta tesi, avaluant el sistema RAH resultant, completament adaptat per streaming, en conjunts de dades acadèmiques àmpliament utilitzades i desafiants tasques basades en continguts audiovisuals reals. Com a resultat, el sistema proporciona baixes taxes d'error amb un reduït temps de resposta, comparables al sistema offline. / [EN] Over the last decade, the media have experienced a revolution, turning away from the conventional TV in favor of on-demand platforms. In addition, this media revolution not only changed the way entertainment is conceived but also how learning is conducted. Indeed, on-demand educational platforms have also proliferated and are now providing educational resources on diverse topics. These new ways to distribute content have come along with requirements to improve accessibility, particularly related to hearing difficulties and language barriers. Here is the opportunity for automatic speech recognition (ASR) to comply with these requirements by providing high-quality automatic captioning. Automatic captioning provides a sound basis for diminishing the accessibility gap, especially for live or streaming content. To this end, streaming ASR must work under strict real-time conditions, providing captions as fast as possible, and working with limited context. However, this limited context usually leads to a quality degradation as compared to the pre-recorded or offline content. This thesis is aimed at developing low-latency streaming ASR with a quality similar to offline ASR. More precisely, it describes the path followed from an initial hybrid offline system to an efficient streaming-adapted system. The first step is to perform a single recognition pass using a state-of-the-art neural network-based language model. In conventional multi-pass systems, this model is often deferred to the second or later pass due to its computational complexity. As with the language model, the neural-based acoustic model is also properly adapted to work with limited context. The adaptation and integration of these models is thoroughly described and assessed using fully-fledged streaming systems on well-known academic and challenging real-world benchmarks. In brief, it is shown that the proposed adaptation of the language and acoustic models allows the streaming-adapted system to reach the accuracy of the initial offline system with low latency. / Jorge Cano, J. (2022). Streaming Automatic Speech Recognition with Hybrid Architectures and Deep Neural Network Models [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/191001 / Compendio

This study was conceptualised within a social-constructivist ontological orientation and, further, uses an interpretive epistemological lens to extract information from the participants who are coming from different life worlds. This thesis, Effective assessment in open distance and e-learning: using the Signature Courses at the University of South Africa as a model for future practice, investigated how emerging information communication technologies (ICTs) can be used to transform, enhance and influence student assessment practices in Open Distance and e-Learning (ODeL) contexts. The ultimate objective of the study was to establish assessment guidelines for effective student assessment in distance education using technology as an enabler. To achieve the objectives of this study, a mixed methods research methodology was adopted in which Unisa lecturers’ and first-year students’ experiences, perceptions, attitudes, and beliefs regarding the use of ICT as a tool to enhance and influence student assessment were sought. Despite some limitations, the study was able to reveal that technology has the potential to influence student-lecturer, and student-peer interaction thereby bridging the isolation gap that normally exists between them. Further, these potential benefits also include the identification of teaching strengths and weaknesses, the indication of areas where instructional change or modification is needed, and the application of more effective means of interacting with students. A key function of this study, therefore, is to help the lecturers involved in higher learning assessment to use technology effectively and efficiently to enhance assessment practices as a means of maintaining both the academic standards and enhancing the quality of the student learning experience. In addition, the study has shown that technology has the potential to enhance and influence student learning and motivation. Furthermore, this study made theoretical and practical contributions to the literature on information communication technology implementation on lecturers’ and students’ pedagogical and technological readiness to online learning and assessment in open distance and e-learning. / Curriculum and Instructional Studies / D. Ed. (Curriculum Studies)